An Improved Integrated Active Contour Model without Re-Initialization for Vector-Valued Images Segmentation

2012 ◽  
Vol 429 ◽  
pp. 271-276 ◽  
Author(s):  
Ji Zhao ◽  
Fu Qun Shao ◽  
Ji Zhao ◽  
Xue Dong Zhang ◽  
Chuang Feng
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Nonlinear Heat Equation ◽  
Level Set Function ◽  
Implementation Scheme ◽  
Level Set Evolution ◽  
Set Function ◽  
Vector Images

In this paper, an improved variational formulation for active contours model is introduced to force level set function to become fast and stably close to signed distance function, which can completely eliminate the need of the costly re-initialization procedure. A restriction item that is a nonlinear heat equation with balanced diffusion rate is attached to variational Integrated Active Contour (IAC) model on the basis of analysis on regions and edges information from all channels of the valued-vector images, so that the level set evolution segmentation process becomes fast and stable. In addition, more efficient discretization method with spatial rotation-invariance gradient and divergence operator is proposed as numerical implementation scheme. Finally, the experiments on some images have demonstrated the efficiency, accuracy and robustness of the proposed method.

scholarly journals Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

2017 ◽  
Vol 17 (4) ◽  
pp. 165-182 ◽  
Author(s):  
Abdallah Azizi ◽  
Kaouther Elkourd ◽  
Zineb Azizi
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Local Binary Pattern ◽  
Active Contour Model ◽  
Intensity Variation ◽  
Level Set Function ◽  
Contour Model ◽  
Local Intensity ◽  
Edge Based ◽  
Set Function

AbstractEdge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

scholarly journals Image Segmentation Using Signed Pressure Force Based Active Contour Model

2013 ◽  
Vol 12 (1) ◽  
pp. 3195-3200
Author(s):  
Sagar Chouksey ◽  
Mayur Ghadle ◽  
Shreya Sharma ◽  
Rohan Puranik
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Computational Cost ◽  
Initial Contour ◽  
Pressure Force ◽  
Binary Function ◽  
Level Set Function ◽  
Contour Model ◽  
Set Function

A novel signed pressure force (SPF) based active contour model (ACM) is proposed in this work. It is implemented with help of Gaussian filtering regularized level set method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of this method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed SPF with ACM has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient. The computational cost for traditional re-initialization can also be reduced.

Edge Based Level Set with Gaussian Filtering Regularized and its Application in Liver Segmentation

2010 ◽  
Vol 121-122 ◽  
pp. 222-227 ◽  
Author(s):  
Rui Jie Feng ◽  
Hui Yan Jiang
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Synthetic Image ◽  
Gaussian Filter ◽  
Gaussian Filtering ◽  
Level Set Function ◽  
Contour Model ◽  
Edge Based

A novel edge-based active contour model (ACM) is proposed in this paper. Our edge-based active contour model has many advantages over the conventional active contour models. Firstly, the proposed model can get much smoother contour and needs much less iterations to evolution by being implemented with a special processing named Selectively Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method. Secondly, we introduce Bilateral Gaussian Filter which can preserve edges to smooth images. So we make weak edges more clear than traditional Gaussian Filter. Thirdly, the level set function can be easily initialized with binary function, which is more efficient to construct than the widely used signed distance function (SDF) because of the special processing. Experiments on synthetic image and segmenting liver from abdominal CT images demonstrate the advantages of the proposed method over geodesic active contours (GAC) in term of both efficiency and accuracy.

Image Segmentation Using Binary Level Set Method Based on Region-Based GAC Model

2011 ◽  
Vol 480-481 ◽  
pp. 1206-1209 ◽  
Author(s):  
Ge Ren ◽  
Xing Qin Cao ◽  
Wei Min Pan ◽  
Yong Yang
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Computational Cost ◽  
Geodesic Active Contour ◽  
Level Set Function ◽  
Contour Model ◽  
Set Function ◽  
Better Than

A new Region-based GAC (geodesic active contour) model was presented, which is the improvement of traditional GAC model. A new region-based signed pressure forces function was presented, which takes the place of the edge stopping function, and can efficiently solve the problem of segmentation of objects with weak edges or without edges. The model is implemented by level set method with a binary level set function to reduce the expensive computational cost of re-initialization of the traditional level set function. The proposed algorithm has been applied to images of different modalities with promising results, which are better than that of traditional GAC model and C-V model.

Active Contour Driven by Edge and Region Image Fitting Energy

2012 ◽  
Vol 532-533 ◽  
pp. 892-896
Author(s):  
Hai Yong Xu ◽  
Ming Hua Liu
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Global Information ◽  
Geodesic Curve ◽  
Regularization Term ◽  
Level Set Function ◽  
Contour Model ◽  
Set Function

In this paper, we propose a novel edge and region-based active contour model. We consider geodesic curve and region-based model, and evolve a contour based on global information. Moreover, we add to the level set regularization term in the energy functional to ensure accurate computation and avoids expensive re-initialization of the level set function. Experiments on synthetic and real images show desirable performances of our method.

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

Kantorovich-Rubinstein misfit for inverting gravity-gradient data by the level-set method

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. G55-G73
Author(s):  
Guanghui Huang ◽  
Xinming Zhang ◽  
Jianliang Qian
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Random Noise ◽  
Magnetic Data ◽  
Gravity Gradient ◽  
Local Minima ◽  
Target Model ◽  
Level Set Function ◽  
Level Set Evolution ◽  
Set Function

We have developed a novel Kantorovich-Rubinstein (KR) norm-based misfit function to measure the mismatch between gravity-gradient data for the inverse gradiometry problem. Under the assumption that an anomalous mass body has an unknown compact support with a prescribed constant value of density contrast, we implicitly parameterize the unknown mass body by a level-set function. Because the geometry of an underlying anomalous mass body may experience various changes during inversion in terms of level-set evolution, the classic least-squares ([Formula: see text]-norm-based) and the [Formula: see text]-norm-based misfit functions for governing the level-set evolution may potentially induce local minima if an initial guess of the level-set function is far from that of the target model. The KR norm from the optimal transport theory computes the data misfit by comparing the modeled data and the measured data in a global manner, leading to better resolution of the differences between the inverted model and the target model. Combining the KR norm with the level-set method yields a new effective methodology that is not only able to mitigate local minima but is also robust against random noise for the inverse gradiometry problem. Numerical experiments further demonstrate that the new KR norm-based misfit function is able to recover deep dipping flanks of SEG/EAGE salt models even at extremely low signal-to-noise ratios. The new methodology can be readily applied to gravity and magnetic data as well.

scholarly journals Segmentation of Regions of Interest Using Active Contours with SPF Function

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Farhan Akram ◽  
Jeong Heon Kim ◽  
Chan-Gun Lee ◽  
Kwang Nam Choi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
High Intensity ◽  
Active Contours ◽  
Regions Of Interest ◽  
Gaussian Kernel ◽  
Level Set Function ◽  
Edge Based ◽  
Inner Region ◽  
Set Function

Segmentation of regions of interest is a well-known problem in image segmentation. This paper presents a region-based image segmentation technique using active contours with signed pressure force (SPF) function. The proposed algorithm contemporaneously traces high intensity or dense regions in an image by evolving the contour inwards. In medical image modalities these high intensity or dense regions refer to tumor, masses, or dense tissues. The proposed method partitions an image into an arbitrary number of subregions and tracks down salient regions step by step. It is implemented by enforcing a new region-based SPF function in a traditional edge-based level set model. It partitions an image into subregions and then discards outer subregion and partitions inner region into two more subregions; this continues iteratively until a stopping condition is fulfilled. A Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed segmentation algorithm has been applied to different images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.

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